Digital images, especially those that have been generated by a capture device such as, but not limited to, a scanner and a camera, contain noise that is encoded in pixels of the image. For example, imprecise electronics, photon noise, and digitization may introduce random noise (RN) during image capture. Fixed pattern noise (FPN) may be introduced by microscopic structured defects of an image sensor of the scanner or camera used to capture the digital image. Such FPN may ‘beat’ with various structures represented in the digital image itself resulting in moiré patterns and other distortion. Noise in images may interfere with image compression as well as printing and viewing the image. Often, the effects of noise (both RN and FPN) are described as ‘grain’ referring to an analogous visual quality associated with film-based images.
Image filtering, especially image smoothing is used extensively to improve an overall visual quality of digital images. In particular, image smoothing may be used to reduce effects of both RN and FPN. For example, classical mean, median, Gaussian, and LoG filters may be employed to smooth an image and reduce apparent digital image grain. However, smoothing does reduce sharpness and associated visual quality of edges in the digital image.
Various approaches have been introduced in image processing to adaptively smooth while preserving edges in the digital image. Specifically, an amount of smoothing may be adjusted in various parts of an image based on an edge content of the digital image. In other cases, edge content of the digital image may be handled differently than other aspects of the digital image during filtering. For example, smoothing may be applied to the digital image and then the edges may be re-introduced or reconstructed. Unfortunately, adaptive smoothing that preserves edges generally requires computationally intensive image processing. Such methods are generally complex and require considerable processing time to perform the adaptive smoothing. Furthermore, multi-channel images often require even more processing time as each channel is handled independently. A way of adaptively smoothing or degraining a digital image, which preserves edges, that is relatively fast and easy to implement and is applicable to multi-channel images would meet a long felt need in the area of image filtering.